84 lines
2.5 KiB
Markdown
84 lines
2.5 KiB
Markdown
# PartPacker
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### [Project Page](https://research.nvidia.com/labs/dir/partpacker/) | [Arxiv](https://arxiv.org/abs/2506.09980) | [Models](https://huggingface.co/nvidia/PartPacker) | [Demo](https://huggingface.co/spaces/nvidia/PartPacker)
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This is the official implementation of *PartPacker: Efficient Part-level 3D Object Generation via Dual Volume Packing*.
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Our model performs part-level 3D object generation from single-view images.
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### Install
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We rely on `torch` with CUDA installed correctly.
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```bash
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pip install -r requirements.txt
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# if you prefer fixed version of dependencies:
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pip install -r requirements.lock.txt
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# by default we use torch's built-in attention, if you want to explicitly use flash-attn:
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pip install flash-attn --no-build-isolation
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# if you want to run data processing and vae inference, please install meshiki:
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pip install meshiki
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```
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### Pretrained models
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Download the pretrained models from huggingface, and put them in the `pretrained` folder.
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```bash
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mkdir pretrained
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cd pretrained
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wget https://huggingface.co/nvidia/PartPacker/resolve/main/vae.pt
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wget https://huggingface.co/nvidia/PartPacker/resolve/main/flow.pt
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```
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### Inference
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For inference, it takes ~16GB GPU memory (assuming float16).
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```bash
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# vae reconstruction of meshes
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PYTHONPATH=. python vae/scripts/infer.py --ckpt_path pretrained/vae.pt --input assets/meshes/ --output_dir output/
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# flow 3D generation from images
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PYTHONPATH=. python flow/scripts/infer.py --ckpt_path pretrained/flow.pt --input assets/images/ --output_dir output/
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# open local gradio app
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python app.py
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```
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### Data Processing
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We provide a *Dual Volume Packing* implementation to process raw glb meshes into two separate meshes as proposed in the paper.
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```bash
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cd data
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python bipartite_contraction.py ./example_mesh.glb
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# the two separate meshes will be saved in ./output
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```
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### Acknowledgements
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This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
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* [Dora](https://github.com/Seed3D/Dora)
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* [Hunyuan3D-2](https://github.com/Tencent/Hunyuan3D-2)
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* [Trellis](https://github.com/microsoft/TRELLIS)
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## Citation
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```
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@article{tang2024partpacker,
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title={Efficient Part-level 3D Object Generation via Dual Volume Packing},
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author={Tang, Jiaxiang and Lu, Ruijie and Li, Zhaoshuo and Hao, Zekun and Li, Xuan and Wei, Fangyin and Song, Shuran and Zeng, Gang and Liu, Ming-Yu and Lin, Tsung-Yi},
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journal={arXiv preprint arXiv:2506.09980},
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year={2025}
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}
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```
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